Paper Info
Title
Combining Convolutional and Recurrent Neural Networks for Classification of Focal Liver Lesions in Multi-phase CT Images.
Abstract
Computer-aided diagnosis (CAD) systems are useful for assisting radiologists with clinical diagnoses by classifying focal liver lesions (FLLs) based on multi-phase computed tomography (CT) images. Although many studies have conducted in the field, there still remain two challenges. First, the temporal enhancement pattern is hard to represent effectively. Second, the local and global information of lesions both are necessary for this task. In this paper, we proposed a framework based on deep learning, called ResGL-BDLSTM, which combines a residual deep neural network (ResNet) with global and local pathways (ResGL Net) with a bi-directional long short-term memory (BDLSTM) model for the task of focal liver lesions classification in multi-phase CT images. In addition, we proposed a novel loss function to train the proposed framework. The loss function is composed of an inter-loss and intra-loss, which can improve the robustness of the framework. The proposed framework outperforms state-of-the-art approaches by achieving a 90.93% mean accuracy.
Year
DOI
Venue
2018
10.1007/978-3-030-00934-2_74
Lecture Notes in Computer Science
Keywords
Field
DocType
Deep learning,ResGLNet,BD-LSTM,Liver lesions classification,Computer-aid diagnosis (CAD) system
CAD,Computer vision,Residual,Pattern recognition,Computer science,Multi phase,Recurrent neural network,Robustness (computer science),Artificial intelligence,Deep learning,Artificial neural network,Medical diagnosis
Conference
Volume
ISSN
Citations 
11071
0302-9743
2
PageRank 
References 
Authors
0.39
11
8
Name
Order
Citations
PageRank
Liang Dong132652.32
Lanfen Lin27824.70
Hongjie Hu3119.50
Qiaowei Zhang462.85
Qingqing Chen563.86
Yutaro lwamoto640.79
Xian-Hua Han71410.19
Yen-Wei Chen8720155.73